Fast magnetic resonance elastography with multiphase radial encoding and harmonic motion sparsity based reconstruction

Wang, Runke; Chen, Yu; Li, Ruokun; Qiu, Suhao; Zhang, Zhiyong; Yan, Fuhua; Feng, Yuan

doi:10.1088/1361-6560/ac4a42

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…The corresponding movement of the tissue produces an accumulation of phase

ϕ

, which is described by the following formula:

ϕ (θ) = γ \int_{0}^{\frac{2 italicπN}{ω}} {bold-italicG}_{bold-italicenc} ∙ r (θ) dt = \frac{italicγπN}{ω} {bold-italicG}_{0} ∙ {bold-italicu}_{0} \sin (k ∙ r - θ)

where

γ

is the gyromagnetic ratio,

ω

is the vibration frequency,

N

is the number of cycles encoded,

{bold-italicG}_{bold-italicenc} = {bold-italicG}_{bold0} \sin (italicωt)

is the MEG,

r

is the spin position,

{bold-italicr}_{bold0}

is the initial position of the spin before vibration,

{bold-italicu}_{0}

is the amplitude of vibration,

k

is the wave number, and

θ

is the vibration phase offset, where

r = {bold-italicr}_{bold0} + {bold-italicu}_{0} \cos (ωt - k ∙ r + θ)

. The most commonly used MRE sequences are based on gradient echo or echo planar imaging sequences, 45 and there have been many enhancements to improve motion encoding efficiency, such as fractional encoding, 46 sample interval modulation, 47 Hadamard encoding 48 and DENSE‐based encoding 49 …”

Section: Overview Of Brain Mrementioning

confidence: 99%

“…The most commonly used MRE sequences are based on gradient echo or echo planar imaging sequences, 45 and there have been many enhancements to improve motion encoding efficiency, such as fractional encoding, 46 sample interval modulation, 47 Hadamard encoding 48 and DENSE-based encoding. 49 A number of assumptions are typically applied, such as small strain, linearity, isotropy, homogeneity, and near incompressibility, in order to obtain values for the biomechanical properties of soft tissue. If the tissue is assumed to be entirely elastic with no viscosity component the equation describing the shear wave propagation is simplified so that shear stiffness is μ ¼ ρV 2 , where, V is the shear wave velocity and ρ is tissue density, which is usually taken to be the same as water which is 1 kg/m 3 .…”

Section: Overview Of Brain Mrementioning

confidence: 99%

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Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Feng

Murphy

Hojo

et al. 2023

Magnetic Resonance Imaging

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Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) present a major health burden to society. Changes in brain structure and cognition are generally only observed at the late stage of the disease. Although advanced magnetic resonance imaging (MRI) techniques such as diffusion imaging may allow identification of biomarkers at earlier stages of neurodegeneration, early diagnosis is still challenging. Magnetic resonance elastography (MRE) is a noninvasive MRI technique for studying the mechanical properties of tissues by measuring the wave propagation induced in the tissues using a purpose‐built actuator. Here, we present a systematic review of preclinical and clinical studies in which MRE has been applied to study neurodegenerative diseases. Actuator systems for data acquisition, inversion algorithms for data analysis, and sample demographics are described and tissue stiffness measures obtained for the whole brain and internal structures are summarized. A total of six animal studies and eight human studies have been published. The animal studies refer to 123 experimental animals (68 AD and 55 PD) and 121 wild‐type animals, while the human studies refer to 142 patients with neurodegenerative disease (including 56 AD and 17 PD) and 166 controls. The animal studies are consistent in the reporting of decreased stiffness of the hippocampal region in AD mice. However, in terms of disease progression, although consistent decreases in either storage modulus or shear modulus magnitude are reported for whole brain, there is variation in the results reported for the hippocampal region. The clinical studies are consistent in reports of a significant decrease in either whole brain storage modulus or shear modulus magnitude, in both AD and PD and with different brain structures affected in different neurodegenerative diseases. MRE studies of neurodegenerative diseases are still in their infancy, and in future it will be interesting to investigate potential relationships between brain mechanical properties and clinical measures, which may help elucidate the mechanisms underlying onset and progression of neurodegenerative diseases.Evidence Level1.Technical EfficacyStage 2.

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“…The corresponding movement of the tissue produces an accumulation of phase

ϕ

, which is described by the following formula:

ϕ (θ) = γ \int_{0}^{\frac{2 italicπN}{ω}} {bold-italicG}_{bold-italicenc} ∙ r (θ) dt = \frac{italicγπN}{ω} {bold-italicG}_{0} ∙ {bold-italicu}_{0} \sin (k ∙ r - θ)

where

γ

is the gyromagnetic ratio,

ω

is the vibration frequency,

N

is the number of cycles encoded,

{bold-italicG}_{bold-italicenc} = {bold-italicG}_{bold0} \sin (italicωt)

is the MEG,

r

is the spin position,

{bold-italicr}_{bold0}

is the initial position of the spin before vibration,

{bold-italicu}_{0}

is the amplitude of vibration,

k

is the wave number, and

θ

is the vibration phase offset, where

r = {bold-italicr}_{bold0} + {bold-italicu}_{0} \cos (ωt - k ∙ r + θ)

Section: Overview Of Brain Mrementioning

confidence: 99%

Section: Overview Of Brain Mrementioning

confidence: 99%

Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Feng

Murphy

Hojo

et al. 2023

Magnetic Resonance Imaging

Self Cite

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“…As a result, they showed significant improvement in reconstruction quality for 2D elasticity reconstruction of a homogeneous phantom compared with the conventional CS method. On the other hand, Wang et al 11 proposed a harmonic‐motion sparsity‐based reconstruction of MRE images acquired using a multiphase radial encoding scheme. The reconstruction scheme, termed SH‐GRASP, utilized sparsity over the time domain using a temporal fast Fourier transform (FFT) and a modified temporal total variation regularization.…”

Section: Introductionmentioning

confidence: 99%

Phase‐regularized and displacement‐regularized compressed sensing for fast magnetic resonance elastography

2023

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Liver magnetic resonance elastography (MRE) is a noninvasive stiffness measurement technique that captures the tissue displacement in the phase of the signal. To limit the scanning time to a single breath‐hold, liver MRE usually involves advanced readout techniques such as simultaneous multislice (SMS) or multishot methods. Furthermore, all these readout techniques require additional in‐plane acceleration using either parallel imaging capabilities, such as sensitivity encoding (SENSE), or k‐space undersampling, such as compressed sensing (CS). However, these methods apply a single regularization function on the complex image. This study aims to design and evaluate methods that use separate regularization on the magnitude and phase of MRE to exploit their distinct spatiotemporal characteristics. Specifically, we introduce two compressed sensing methods. The first method, termed phase‐regularized compressed sensing (PRCS), applies a two‐dimensional total variation (TV) prior to the magnitude and two‐dimensional wavelet regularization to the phase. The second method, termed displacement‐regularized compressed sensing (DRCS), exploits the spatiotemporal redundancy using 3D total variation on the magnitude. Additionally, DRCS includes a displacement fitting function to apply wavelet regularization to the displacement phasor. Both DRCS and PRCS were evaluated with different levels of compression factors in three datasets: an in silico abdomen dataset, an in vitro tissue‐mimicking phantom, and an in vivo liver dataset. The reconstructed images were compared with the full sampled reconstruction, zero‐filling reconstruction, wavelet‐regularized compressed sensing, and a low rank plus sparse reconstruction. The metrics used for quantitative evaluation were the structural similarity index (SSIM) of magnitude (M‐SSIM), displacement (D‐SSIM), and shear modulus (S‐SSIM), and mean shear modulus. Results from highly undersampled in silico and in vitro datasets demonstrate that the DRCS method provides higher reconstruction quality than the conventional compressed sensing method for a wide range of stiffness values. Notably, DRCS provides 24% and 22% increase in D‐SSIM compared with CS for the in silico and in vitro datasets, respectively. Comparison with liver stiffness measured from full sampled data and highly undersampled data (CR=4) demonstrates that the DRCS method provided the strongest correlation ( R2=0.95), second‐lowest mean bias (−0.18 kPa, lowest for CS with −0.16 kPa), and lowest coefficient of variation (CV=3.6%). Our results demonstrate the potential of using DRCS to improve the reconstruction quality of accelerated MRE.

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A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding

Wang,

Chen,

Yan

et al. 2024

Magnetic Resonance in Med

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PurposesTo enhance the functional capability of MRI, this study aims to develop a novel MR elastography (MRE) sequence that achieves rapid acquisition without distortion artifacts.MethodsA displacement‐encoded stimulated echo (DENSE) with multiphase acquisition scheme was used to capture wave images. A center‐out golden‐angle stack‐of‐stars sampling pattern was introduced for improved SNR and data incoherence. A combination of Hadamard encoding and interleaved multislab acquisition schemes was used to increase the acquisition efficiency of MRE data with multiple directions and phase offsets. A generalized parallel‐imaging and compressed‐sensing method was further applied to accelerate the acquisition process. The imaging results of the proposed sequence were compared with those from six gradient echo (GRE)/EPI/DENSE–based MRE sequences via phantom and brain acquisitions.ResultsThe proposed sequence achieved a 6‐fold acceleration compared with GRE MRE. With the application of a conventional parallel‐imaging and compressed‐sensing algorithm, the scanning speed was further accelerated by 8‐fold, matching the speed of EPI‐based MRE. Phantom tests revealed small variances in stiffness measurements across the seven sequences (< 9.23%). The proposed sequence exhibited a higher contrast‐to‐noise ratio (1.38) than the two EPI‐based sequences (0.61/0.76) and similar to GRE‐based sequences (1.34/1.22/1.58). Brain imaging validated the effectiveness of the proposed sequence in accurate stiffness estimation and distortion artifact avoidance.ConclusionA rapid DENSE‐based MRE sequence with interleaved multislab acquisition and Hadamard encoding was developed at a speed matching EPI‐based sequences, without compromising SNR or introducing distortion artifacts.

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Fast magnetic resonance elastography with multiphase radial encoding and harmonic motion sparsity based reconstruction

Cited by 5 publications

References 53 publications

Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Phase‐regularized and displacement‐regularized compressed sensing for fast magnetic resonance elastography

A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding

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